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FindAdapt: A python package for fast and accurate adapter detection in small RNA sequencing

PLOS COMPUTATIONAL BIOLOGY 2024年第1期20卷 e1011786页

作者： Chen, Hua-Chang Wang, Jing Shyr, Yu Liu, Qi Vanderbilt Univ Med Ctr Dept Biostat Nashville TN 37232 USA Vanderbilt Univ Med Ctr Ctr Quantitat Sci Nashville TN 37232 USA

Adapter trimming is an essential step for analyzing small RNA sequencing data, where reads are generally longer than target RNAs ranging from 18 to 30 bp. Most adapter trimming tools require adapter information as input. However, adapter information is hard to access, specified incorrectly, or not provided with publicly available datasets, hampering their reproducibility and reusability. Manual identification of adapter patterns from raw reads is labor-intensive and error-prone. Moreover, the use of randomized adapters to reduce ligation biases during library preparation makes adapter detection even more challenging. Here, we present FindAdapt, a Python package for fast and accurate detection of adapter patterns without relying on prior information. We demonstrated that FindAdapt was far superior to existing approaches. It identified adapters successfully in 180 simulation datasets with diverse read structures and 3,184 real datasets covering a variety of commercial and customized small RNA library preparation kits. FindAdapt is stand-alone software that can be easily integrated into small RNA sequencing analysis pipelines.

关键词： MicroRNAs RNA sequencing RNA structure Small interfering RNA RNA sequences cDNA libraries blast algorithm Sequence motif analysis

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An overview of bioinformatics courses delivered at the academic level in Italy: Reflections and recommendations from BITS

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PLOS COMPUTATIONAL BIOLOGY 2023年第2期19卷 e1010846-e1010846页

作者： Marangoni, Roberto Bevilacqua, Vitoantonio Cannataro, Mario Mele, Bruno Hay Mauri, Giancarlo Marabotti, Anna Bioinformat Italian Soc BITS Rome Italy Univ Pisa Dept Biol Pisa Italy Polytech Univ Bari Dept Elect & Informat Engn Bary Italy Magna Graecia Univ Catanzaro Dept Med & Surg Sci Catanzaro Italy Univ Naples Federico II Dept Biol Naples Italy Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy Univ Salerno Dept Chem & Biol A Zambelli Fisciano Italy

In Italian universities, bioinformatics courses are increasingly being incorporated into different study paths. However, the content of bioinformatics courses is usually selected by the professor teaching the course, in the absence of national guidelines that identify the minimum indispensable knowledge in bioinformatics that undergraduate students from different scientific fields should achieve. The Training&Teaching group of the Bioinformatics Italian Society (BITS) proposed to university professors a survey aimed at portraying the current situation of bioinformatics courses within undergraduate curricula in Italy (i.e., bioinformatics courses activated within both bachelor's and master's degrees). Furthermore, the Training&Teaching group took a cue from the survey outcomes to develop recommendations for the design and the inclusion of bioinformatics courses in academic curricula. Here, we present the outcomes of the survey, as well as the BITS recommendations, with the hope that they may support BITS members in identifying learning outcomes and selecting content for their bioinformatics courses. As we share our effort with the broader international community involved in teaching bioinformatics at academic level, we seek feedback and thoughts on our proposal and hope to start a fruitful debate on the topic, including how to better fulfill the real bioinformatics knowledge needs of the research and the labor market at both the national and international level.

关键词： Bioinformatics Protein structure comparison Sequence alignment blast algorithm Heuristic alignment procedure Multiple alignment calculation Computer and information sciences Proteomic databases

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MetaNovo: An open-source pipeline for probabilistic peptide discovery in complex metaproteomic datasets

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PLOS COMPUTATIONAL BIOLOGY 2023年第6期19卷 e1011163-e1011163页

作者： Potgieter, Matthys J. Nel, Andrew J. M. M. Fortuin, Suereta Garnett, Shaun Wendoh, Jerome Tabb, David Mulder, Nicola Blackburn, Jonathan Univ Cape Town Dept Integrat Biomed Sci Computat Biol Div Cape Town South Africa Univ Cape Town Dept Integrat Biomed Sci Div Chem & Syst Biol Cape Town South Africa Univ Cape Town Dept Pathol Div Immunol Cape Town South Africa Stellenbosch Univ Dept Biomed Sci Div Mol Biol & Human Genet Cape Town South Africa Stellenbosch Univ African Microbiome Inst Cape Town South Africa Stellenbosch Univ South African TB Bioinformat Initiat Cape Town South Africa Univ Cape Town Inst Infect Dis & Mol Med Fac Hlth Sci Cape Town South Africa

Author summaryMetaNovo is an open-source software pipeline that integrates existing tools with a custom algorithm to produce targeted protein sequence databases for mass spectrometry metaproteomic analysis as an intermediate filtering step prior to standard sequence database search approaches. MetaNovo uses open-source tools to match peptide mass spectrometry spectra to sequence database entries in a parallelised and scalable manner and can be installed in a cluster or run standalone on a Linux machine. The software is scalable to the number of input files and search sequence database size. As inputs the software requires raw mass spectrometry data in MGF format, and a UniProt FASTA sequence database to search. The pipeline is relevant to users analysing protein data from multiple organisms, where the exact species composition is unknown, such as microbiome or environmental samples, and provides an avenue for analysis when matched metagenomics data or accurate taxonomic characterisation is not available as it infers the organisms and proteins present directly from the raw data and the parent sequence database. The targeted sequence database produced can be used with standard downstream peptide identification software that relies on a targeted input sequence database to search the raw data against and allows greater sensitivity in peptide spectral matching in metaproteomic datasets. BackgroundMicrobiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the pro

关键词： Sequence databases Database searching Metagenomics Taxonomy Microbiome blast algorithm Open source software Proteomes

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The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions

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PLOS COMPUTATIONAL BIOLOGY 2023年第8期19卷 e1011371-e1011371页

作者： Tec-Campos, Diego Posadas, Camila Tibocha-Bonilla, Juan D. Thiruppathy, Deepan Glonek, Nathan Zuniga, Cristal Zepeda, Alejandro Zengler, Karsten Univ Autonoma Yucatan Fac Ingn Quim Merida Yucatan Mexico Univ Calif San Diego Dept Pediat La Jolla CA 92093 USA Univ Calif San Diego Bioinformat & Syst Biol Grad Program La Jolla CA USA Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA Univ Calif San Diego Ctr Microbiome Innovat La Jolla CA 92093 USA

The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between

关键词： Nitrogen metabolism Metabolites Metabolic pathways Nitrogen Protein metabolism Photosynthesis blast algorithm Enzyme metabolism

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DPCfam: Unsupervised protein family classification by Density Peak Clustering of large sequence datasets

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PLOS ONE 2022年第10期17卷 e1010610页

作者： Russo, Elena Tea Barone, Federico Bateman, Alex Cozzini, Stefano Punta, Marco Laio, Alessandro SISSA Trieste Italy Area Sci Pk Trieste Italy Univ Trieste Dept Math & Geosci Trieste Italy European Bioinformat Inst EBI European Mol Biol Lab EMBL Wellcome Genome Campus Hinxton England IRCCS San Raffaele Hosp Ctr Omics Sci Milan Italy IRCCS San Raffaele Sci Inst Div Immunol Transplantat & Infect Dis Unit Immunogenet Leukemia Genom & Immunobiol Milan Italy Abdus Salaam Int Ctr Theoret Phys Trieste Italy

Proteins that are known only at a sequence level outnumber those with an experimental characterization by orders of magnitude. Classifying protein regions (domains) into homologous families can generate testable functional hypotheses for yet unannotated sequences. Existing domain family resources typically use at least some degree of manual curation: they grow slowly over time and leave a large fraction of the protein sequence space unclassified. We here describe automatic clustering by Density Peak Clustering of UniRef50 v. 2017_07, a protein sequence database including approximately 23M sequences. We performed a radical re-implementation of a pipeline we previously developed in order to allow handling millions of sequences and data volumes of the order of 3 TeraBytes. The modified pipeline, which we call DPCfam, finds similar to 45,000 protein clusters in UniRef50. Our automatic classification is in close correspondence to the ones of the Pfam and ECOD resources: in particular, about 81% of medium-large Pfam families and 80% of ECOD families can be mapped to clusters generated by DPCfam. In addition, our protocol finds more than 14,000 clusters constituted of protein regions with no Pfam annotation, which are therefore candidates for representing novel protein families. These results are made available to the scientific community through a dedicated repository.

关键词： Protein domains Sequence alignment Protein structure comparison blast algorithm Molecular evolution Protein structure databases Protein structure Protein structure prediction

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HELIOS: High-speed sequence alignment in optics

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PLOS COMPUTATIONAL BIOLOGY 2022年第11期18卷 e1010665页

作者： Maleki, Ehsan Abadi, Saeedeh Akbari Rokn Koohi, Somayyeh Sharif Univ Technol Dept Comp Engn Tehran Iran

In response to the imperfections of current sequence alignment methods, originated from the inherent serialism within their corresponding electrical systems, a few optical approaches for biological data comparison have been proposed recently. However, due to their low performance, raised from their inefficient coding scheme, this paper presents a novel all-optical high-throughput method for aligning DNA, RNA, and protein sequences, named HELIOS. The HELIOS method employs highly sophisticated operations to locate character matches, single or multiple mutations, and single or multiple indels within various biological sequences. On the other hand, the HELIOS optical architecture exploits high-speed processing and operational parallelism in optics, by adopting wavelength and polarization of optical beams. For evaluation, the functionality and accuracy of the HELIOS method are approved through behavioral and optical simulation studies, while its complexity and performance are estimated through analytical computation. The accuracy evaluations indicate that the HELIOS method achieves a precise pairwise alignment of two sequences, highly similar to those of Smith-Waterman, Needleman-Wunsch, blast, MUSCLE, ClustalW, Clustal omega, T-Coffee, Kalign, and MAFFT. According to our performance evaluations, the HELIOS optical architecture outperforms all alternative electrical and optical algorithms in terms of processing time and memory requirement, relying on its highly sophisticated method and optical architecture. Moreover, the employed compact coding scheme highly escalates the number of input characters, and hence, it offers reduced time and space complexities, compared to the electrical and optical alternatives. It makes the HELIOS method and optical architecture highly applicable for biomedical applications. Author summaryThe character-by-character alignment of two long biological sequences, i.e. DNA, RNA, and protein, is a tedious task, but essential for recognizing homolo

关键词： Sequence alignment Gorillas blast algorithm Chimpanzees Plant genomics Blue whales Fin whales Human genomics

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Constructing benchmark test sets for biological sequence analysis using independent set algorithms

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PLOS COMPUTATIONAL BIOLOGY 2022年第3期18卷 e1009492-e1009492页

作者： Petti, Samantha Eddy, Sean R. Harvard Univ NSF Simons Ctr Math & Stat Anal Biol Cambridge MA USA Howard Hughes Med Inst Chevy Chase MD 20815 USA Harvard Univ Dept Mol & Cellular Biol Cambridge MA 02138 USA Harvard Univ John Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA

Biological sequence families contain many sequences that are very similar to each other because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.

关键词： Cobalt Machine learning algorithms Sequence alignment algorithms blast algorithm Multiple alignment calculation Machine learning Protein domains

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Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction

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PLOS COMPUTATIONAL BIOLOGY 2022年第12期18卷 e1010793页

作者： Zhu, Yi-Heng Zhang, Chengxin Yu, Dong-Jun Zhang, Yang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA Univ Michigan Dept Biol Chem Ann Arbor MI 48109 USA

Accurate identification of protein function is critical to elucidate life mechanisms and design new drugs. We proposed a novel deep-learning method, ATGO, to predict Gene Ontology (GO) attributes of proteins through a triplet neural-network architecture embedded with pre-trained language models from protein sequences. The method was systematically tested on 1068 non-redundant benchmarking proteins and 3328 targets from the third Critical Assessment of Protein Function Annotation (CAFA) challenge. Experimental results showed that ATGO achieved a significant increase of the GO prediction accuracy compared to the state-of-the-art approaches in all aspects of molecular function, biological process, and cellular component. Detailed data analyses showed that the major advantage of ATGO lies in the utilization of pre-trained transformer language models which can extract discriminative functional pattern from the feature embeddings. Meanwhile, the proposed triplet network helps enhance the association of functional similarity with feature similarity in the sequence embedding space. In addition, it was found that the combination of the network scores with the complementary homology-based inferences could further improve the accuracy of the predicted models. These results demonstrated a new avenue for high-accuracy deep-learning function prediction that is applicable to large-scale protein function annotations from sequence alone. Author summary In the post-genome sequencing era, a major challenge in computational molecular biology is to annotate the biological functions of all genes and gene products, which have been classified, in the context of the widely used Gene Ontology (GO), into three aspects of molecular function, biological process, and cellular component. In this work, we proposed a new open-source deep-learning architecture, ATGO, to deduce GO terms of proteins from the primary amino acid sequence, through the integration of the triplet neural-network with pre-tr

关键词： Forecasting Gene ontologies Language blast algorithm Machine learning Sequence alignment Neural networks Protein sequencing

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Remote homology search with hidden Potts models

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PLOS COMPUTATIONAL BIOLOGY 2020年第11期16卷 e1008085-e1008085页

作者： Wilburn, Grey W. Eddy, Sean R. Harvard Univ Dept Phys Cambridge MA 02138 USA Harvard Univ Howard Hughes Med Inst Dept Mol & Cellular Biol Cambridge MA 02138 USA Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA

Author summary Computational homology search and alignment tools are used to infer the functions and evolutionary histories of biological sequences. Most widely used tools for sequence homology searches, such as blast and HMMER, rely on primary sequence conservation alone. It should be possible to make more powerful search tools by also considering higher-order covariation patterns induced by 3D structure conservation. Recent advances in 3D protein structure prediction have used a class of statistical physics models called Potts models to infer pairwise correlation structure in multiple sequence alignments. However, Potts models assume alignments are given and cannot build new alignments, limiting their use in homology search. We have extended Potts models to include a probability model of insertion and deletion so they can be applied to sequence alignment and remote homology search using a new model we call a hidden Potts model (HPM). Tests of our prototype HPM software show promising results in initial benchmarking experiments, though more work will be needed to use HPMs in practical tools. Most methods for biological sequence homology search and alignment work with primary sequence alone, neglecting higher-order correlations. Recently, statistical physics models called Potts models have been used to infer all-by-all pairwise correlations between sites in deep multiple sequence alignments, and these pairwise couplings have improved 3D structure predictions. Here we extend the use of Potts models from structure prediction to sequence alignment and homology search by developing what we call a hidden Potts model (HPM) that merges a Potts emission process to a generative probability model of insertion and deletion. Because an HPM is incompatible with efficient dynamic programming alignment algorithms, we develop an approximate algorithm based on importance sampling, using simpler probabilistic models as proposal distributions. We test an HPM implementation on RNA stru

关键词： Sequence alignment Multiple alignment calculation RNA alignment RNA structure Computer software Protein structure comparison Riboswitches blast algorithm

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Template-based mapping of dynamic motifs in tissue morphogenesis

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PLOS COMPUTATIONAL BIOLOGY 2020年第8期16卷 e1008049-e1008049页

作者： Stern, Tomer Shvartsman, Stanislav Y. Wieschaus, Eric F. Princeton Univ Dept Mol Biol Princeton NJ 08544 USA Princeton Univ Dept Chem & Biol Engn Princeton NJ 08544 USA Princeton Univ Lewis Sigler Inst Integrat Genom Princeton NJ 08544 USA Simons Fdn Flatiron Inst Ctr Computat Biol New York NY USA

Tissue morphogenesis relies on repeated use of dynamic behaviors at the levels of intracellular structures, individual cells, and cell groups. Rapidly accumulating live imaging datasets make it increasingly important to formalize and automate the task of mapping recurrent dynamic behaviors (motifs), as it is done in speech recognition and other data mining applications. Here, we present a "template-based search" approach for accurate mapping of sub- to multi-cellular morphogenetic motifs using a time series data mining framework. We formulated the task of motif mapping as a subsequence matching problem and solved it using dynamic time warping, while relying on high throughput graph-theoretic algorithms for efficient exploration of the search space. This formulation allows our algorithm to accurately identify the complete duration of each instance and automatically label different stages throughout its progress, such as cell cycle phases during cell division. To illustrate our approach, we mapped cell intercalations during germband extension in the earlyDrosophilaembryo. Our framework enabled statistical analysis of intercalary cell behaviors in wild-type and mutant embryos, comparison of temporal dynamics in contracting and growing junctions in different genotypes, and the identification of a novel mode of iterative cell intercalation. Our formulation of tissue morphogenesis using time series opens new avenues for systematic decomposition of tissue morphogenesis. Author summary Much like many ballet dances that are choreographed by combining the same movements in different ways, tissue morphogenesis relies on combinations of highly conserved sub- to multi-cellular behaviors, or motifs, such as cell division, death and intercalation. algorithms for systematic mapping of morphogenetic motifs are essential for making the most of rapidly accumulating live imaging datasets. Here, we present a template-based strategy for motif mapping using an integrated time series data

关键词： Sequence motif analysis algorithms Cell cycle and cell division Morphogenesis blast algorithm Behavior Drosophila melanogaster Embryos

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